AI Tools for Financial Analysis: What Actually Works in 2026

The right AI tool for financial analysis depends on the job in front of you. Reading a 200-page 10-K is a different task from building a DCF, and both differ from scanning 40 competitors' earnings calls. No single tool wins all three.

Here are the quick picks:

• Read and summarize financial documents (10-Ks, earnings reports, scanned statements): PdfGPT, Claude, Google NotebookLM, ChatGPT
• Spreadsheet analysis and modeling: Microsoft Copilot in Excel, Daloopa
• Market and deep research across many sources: AlphaSense, Hebbia, Rogo, Bloomberg's ASKB

This guide groups tools by the work you actually do, so you can match a tool to a task instead of buying a platform and hoping it fits. Prices below are approximate and move often, so treat every figure as a starting point and confirm current pricing on each vendor's site before you commit.

Comparison table

Tool	Best for	Key use	Rough cost (verify)
PdfGPT	Reading financial PDFs	Summarize 10-Ks and earnings reports, extract figures and risk factors, ask questions with page citations, OCR scanned statements	Free tier; paid from ~$4.99/mo
Claude	Long-document analysis	Read a full 100-200 page 10-K in one conversation, compare filings, pull specific disclosures	Free tier; Pro ~$20/mo
ChatGPT	Quick, one-off analysis	Explain ratios, draft Excel formulas, summarize sections of a filing	Free tier; Plus ~$20/mo
Google NotebookLM	Grounded Q&A across your sources	Load filings and transcripts, ask questions with inline page citations	Free; higher limits on paid Google plans
Microsoft Copilot in Excel	Spreadsheet work and modeling	Build DCFs, variance analysis, and month-end close with finance skills and audit trails	Requires Microsoft 365 Copilot add-on
Daloopa	Financial data extraction	Auto-populate and update models with source-linked fundamentals	Enterprise, custom pricing
AlphaSense	Market intelligence	Semantic search across SEC filings, broker research, and expert transcripts	Enterprise, ~$15k-$20k+/seat/yr (est.)
Hebbia	Deep multi-document research	Analyze many filings and calls at once with citation trails	Enterprise, custom pricing
Rogo	Investment banking workflows	Peer comps, company profiles, and pitch materials inside Office	Enterprise, custom pricing
Bloomberg (ASKB)	Terminal-based data queries	Ask for financial data in plain language, get BQL code to extend in Excel	Terminal subscription (high, verify)

Reading and summarizing financial documents

Most financial analysis starts with reading. You open a 10-K, a 10-Q, an earnings transcript, or a scanned bank statement, and you need the numbers and the caveats fast. Four tools handle this job well, and they split by document length and how much you care about citations.

PdfGPT

PdfGPT reads financial documents and answers questions about them with page citations. Upload a 200-page 10-K and you get a summary in seconds, with each point linked back to the page it came from, so you can verify a claim before you cite it in a memo. Ask it to pull the risk factors, find the revenue segment breakdown, or list every mention of goodwill impairment, and it points you to the exact pages.

It handles documents up to 1,200 pages and 25 MB, which covers most annual reports and earnings packs. The OCR reads scanned PDFs in about 10 languages, so a photographed statement or an older filing that exists only as an image becomes searchable and answerable. Deep Research lets you ask one question across several files at once, useful when you compare three years of the same company's filings or two competitors side by side.

Be clear about what PdfGPT is and is not. It reads and analyzes financial documents. It does not build financial models, run spreadsheet calculations, or pull live market data. If your job is to understand what a filing says and back it up with citations, it fits. If your job is to forecast next quarter's cash flow in a model, use a spreadsheet tool below. There is a free tier with no login, and paid plans start around $4.99 a month.

Claude

Claude reads long documents in a single pass. Its large context window lets it take in a full 100-to-200-page 10-K in one conversation, which means you can ask follow-up questions without re-uploading or chunking the file. Analysts use it to compare disclosures across filings, extract specific clauses from credit agreements, and summarize dense footnotes. It has a free tier, with the Pro plan around $20 a month, or roughly $17 a month billed annually.

ChatGPT

ChatGPT is the tool for quick, one-off tasks. It calculates a financial ratio and explains the math in a few seconds, drafts an Excel formula, or summarizes a section of a filing you paste in. One limitation matters for long documents: ChatGPT's in-app context is smaller than the API's, so a full 10-K can still overflow the working context in a normal chat. The reliable approach is to use a reasoning model or feed the filing one section at a time, such as the MD&A, the risk factors, or the financial statements, with a prompt tailored to each. Free tier available, Plus around $20 a month.

Google NotebookLM

NotebookLM grounds its answers in the sources you give it. Load a set of filings, transcripts, and notes into a notebook, then ask questions and get answers with inline citations that point to the exact page. It is free with any Google account, and the free tier holds up to 50 sources per notebook, with each source up to 500,000 words. Paid Google plans raise the source limit. It works well when you want answers restricted to your own document set rather than the open web.

Spreadsheet analysis and modeling

Reading gets you the facts. Modeling turns them into a forecast or a valuation. Two tools stand out for spreadsheet work in 2026, and they solve different halves of the problem.

Microsoft Copilot in Excel

Microsoft added finance-specific features to Copilot in Excel through 2026. You can define skills for repeatable processes, such as building a DCF, refreshing a monthly reporting model, closing the books, or preparing a variance analysis, and Copilot walks through the steps with consistent structure and formatting. New financial data connectors bring outside data into the sheet, including LSEG, Moody's, CB Insights, and Daloopa, and the updates emphasize audit trails so you can trace how a number was produced. Access requires a Microsoft 365 Copilot subscription, which is a paid add-on on top of your Office license, so confirm the current per-seat cost with Microsoft.

Daloopa

Daloopa attacks the tedious part of modeling: getting clean, current data into your spreadsheet. It extracts source-linked fundamental data from SEC filings, investor presentations, and press releases, and its Excel add-in updates your model when new numbers come out. During earnings season, that can save real time per ticker. It connects to ChatGPT, Claude, and Perplexity, and it works as a Copilot in Excel connector. Pricing is not public and targets institutional teams, so expect a custom enterprise quote.

Market and deep research

When the question spans dozens or hundreds of documents, you need search and synthesis across a large corpus. These platforms are built for that scale, and most of them are enterprise-priced.

AlphaSense

AlphaSense runs semantic search across a large library of SEC filings, earnings call transcripts, broker research, and news. Its 2024 acquisition of Tegus added a deep expert-call transcript library, so you can read what management and industry experts have said alongside the filings. AlphaSense publishes no list price. Third-party estimates put standard access around $15,000 to $20,000 and up per seat per year, with tiers that include expert-call transcripts higher, so get a current quote before you plan a budget.

Hebbia

Hebbia is built for investment analysis and document review at scale. Its Matrix product runs analysis across many earnings calls and filings at once and tracks citations for an audit trail, which matters when compliance asks how you reached a conclusion. It integrates with data providers used by deal teams and asset managers. Pricing is enterprise and custom.

Rogo

Rogo targets investment banking workflows. It produces the standard outputs of a banking team, including peer comparisons, company profiles, and pitch materials, and it works inside Excel, PowerPoint, and Word alongside a firm's data warehouse. One caveat: Rogo does not bundle its own proprietary library of expert calls or broker research. It reaches that content through partner integrations and your firm's own data licenses, so you supply and validate more of the sources yourself. Pricing is enterprise and custom.

Bloomberg ASKB

If your desk already runs the Bloomberg Terminal, ASKB lets you query financial data in plain language. It launched in early 2026 as an agentic interface, and when it returns data analysis it also gives you the underlying Bloomberg Query Language code, so you can carry the work into Excel or Bloomberg's own analytics environment. Access comes with a Terminal subscription, which is a significant annual cost per seat, so verify the current figure with Bloomberg.

How to choose the right tool

Start with the task, then the budget, then the trust requirement.

Match the tool to the job. Reading and citing a filing is a document task. Building a forecast is a spreadsheet task. Scanning a whole sector is a research task. Buying an enterprise research platform to summarize one PDF wastes money, and asking a PDF reader to build your model asks it to do something it was not built for.

Set your budget honestly. You can do real work for free or close to it with PdfGPT, ChatGPT, Claude, and NotebookLM. Enterprise platforms like AlphaSense, Hebbia, Rogo, and Daloopa run into five and six figures a year and make sense when a team's time savings justify the spend. If you are a student or an individual analyst, start with the free and low-cost tools.

Demand citations for anything you publish. AI models can state a number confidently and get it wrong. Tools that link each claim to a source page, such as PdfGPT, NotebookLM, and Hebbia, let you verify before you rely on a figure. For financial work, that verification step is not optional. Always check the AI's output against the original document.

Test on your own documents first. A tool that reads a clean 10-K may stumble on a scanned statement or a foreign-language filing. Run a real file from your own workflow through any tool before you standardize on it.

Frequently asked questions

What is the best AI tool for reading a 10-K or earnings report?

For reading and summarizing a single long filing with citations you can check, PdfGPT and Claude both work well. PdfGPT gives you page-linked answers and handles documents up to 1,200 pages, including scanned ones through OCR. Claude reads a full 10-K in one conversation for extended back-and-forth. NotebookLM is a strong free option when you want answers grounded strictly in the sources you upload.

Can AI tools build financial models and do spreadsheet analysis?

Yes, but that is a different category of tool. Microsoft Copilot in Excel builds DCFs, variance analyses, and monthly reporting models inside your spreadsheet, and Daloopa auto-populates models with source-linked data. Document readers like PdfGPT extract the numbers and context from filings, but they do not run spreadsheet calculations or forecasting. Many analysts pair a document reader with a spreadsheet tool.

Are free AI tools good enough for financial analysis?

For individuals and students, often yes. Free tiers of PdfGPT, ChatGPT, Claude, and NotebookLM cover reading, summarizing, ratio math, and grounded Q&A. Enterprise platforms earn their price when you need to search across millions of documents, keep audit trails for compliance, or save a team hundreds of hours during earnings season. Start free, then upgrade only when you hit a real limit.

How do I check whether an AI tool's financial answer is accurate?

Use tools that cite their sources, then open the cited page and confirm the number yourself. PdfGPT links each point to the page it came from, and NotebookLM and Hebbia provide similar citation trails. Treat AI output as a fast first draft that you verify against the original filing, never as a final figure you cite without checking.

Can these tools read scanned or image-based financial statements?

Some can. PdfGPT includes OCR that reads scanned PDFs in about 10 languages, so a photographed or image-only statement becomes searchable and you can ask questions about it. Not every tool handles scanned documents reliably, so if you regularly work with paper-sourced statements, test that specific capability before you rely on it.